Update old links to bitbucket to point to gitlab.com

Eigen/src/Core/arch/SSE/MathFunctions.h

@@ -168,7 +168,7 @@ double sqrt(const double &x)
 {
 #if EIGEN_COMP_GNUC_STRICT
   // This works around a GCC bug generating poor code for _mm_sqrt_pd
-  // See https://bitbucket.org/eigen/eigen/commits/14f468dba4d350d7c19c9b93072e19f7b3df563b
+  // See https://gitlab.com/libeigen/eigen/commit/8dca9f97e38970
   return internal::pfirst(internal::Packet2d(__builtin_ia32_sqrtsd(_mm_set_sd(x))));
 #else
   return internal::pfirst(internal::Packet2d(_mm_sqrt_pd(_mm_set_sd(x))));

README.md

@@ -2,6 +2,4 @@
 
 For more information go to http://eigen.tuxfamily.org/.
 
-For ***pull request*** please only use the official repository at https://bitbucket.org/eigen/eigen.
-
-For ***bug reports*** and ***feature requests*** go to http://eigen.tuxfamily.org/bz.
+For ***pull request***, ***bug reports***, and ***feature requests***, go to https://gitlab.com/libeigen/eigen.

doc/DenseDecompositionBenchmark.dox

@@ -35,7 +35,7 @@ Timings are in \b milliseconds, and factors are relative to the LLT decompositio
  + For large problem sizes, only the decomposition implementing a cache-friendly blocking strategy scale well. Those include LLT, PartialPivLU, HouseholderQR, and BDCSVD. This explain why for a 4k x 4k matrix, HouseholderQR is faster than LDLT. In the future, LDLT and ColPivHouseholderQR will also implement blocking strategies.
  + CompleteOrthogonalDecomposition is based on ColPivHouseholderQR and they thus achieve the same level of performance.
 
-The above table has been generated by the <a href="https://bitbucket.org/eigen/eigen/raw/default/bench/dense_solvers.cpp">bench/dense_solvers.cpp</a> file, feel-free to hack it to generate a table matching your hardware, compiler, and favorite problem sizes.
+The above table has been generated by the <a href="https://gitlab.com/libeigen/eigen/raw/master/bench/dense_solvers.cpp">bench/dense_solvers.cpp</a> file, feel-free to hack it to generate a table matching your hardware, compiler, and favorite problem sizes.
 
 */